{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Distance_matrix.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "Wiq8rtDvYrcA",
        "3JKVlOLYYrcc"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Wiq8rtDvYrcA"
      },
      "source": [
        "## 1. TSP problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EIK3ozN7Yrcp"
      },
      "source": [
        "## 3.Vehicle Routing problem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cttt8x_KMdyc",
        "colab_type": "code",
        "outputId": "8fe94ab0-2f22-4b97-b298-b6df2458c6d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "!pip install ortools"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting ortools\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b2/c2/81a18b1ee4e9c8401fa2c9bc6fcd1b59b1c30ff7a1b214aff84d718d95c5/ortools-7.4.7247-cp36-cp36m-manylinux1_x86_64.whl (27.7MB)\n",
            "\u001b[K     |████████████████████████████████| 27.7MB 110kB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf>=3.10.0 in /usr/local/lib/python3.6/dist-packages (from ortools) (3.10.0)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from ortools) (1.12.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.10.0->ortools) (42.0.2)\n",
            "Installing collected packages: ortools\n",
            "Successfully installed ortools-7.4.7247\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XvPzbY3lYrcq",
        "colab": {}
      },
      "source": [
        "\"\"\"Vehicles Routing Problem (VRP).\"\"\"\n",
        "\n",
        "from __future__ import print_function\n",
        "from ortools.constraint_solver import routing_enums_pb2\n",
        "from ortools.constraint_solver import pywrapcp\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "L3LCXhb4Yrcs",
        "outputId": "e1368232-48d9-44d8-9b38-c11682af43d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        }
      },
      "source": [
        "def create_data_model():\n",
        "    \"\"\"Stores the data for the problem.\"\"\"\n",
        "    data = {}\n",
        "    data['distance_matrix'] = [\n",
        "        [\n",
        "            0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
        "            468, 776, 662\n",
        "        ],\n",
        "        [\n",
        "            548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
        "            1016, 868, 1210\n",
        "        ],\n",
        "        [\n",
        "            776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
        "            1130, 788, 1552, 754\n",
        "        ],\n",
        "        [\n",
        "            696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
        "            1164, 560, 1358\n",
        "        ],\n",
        "        [\n",
        "            582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
        "            1050, 674, 1244\n",
        "        ],\n",
        "        [\n",
        "            274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
        "            514, 1050, 708\n",
        "        ],\n",
        "        [\n",
        "            502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
        "            514, 1278, 480\n",
        "        ],\n",
        "        [\n",
        "            194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
        "            662, 742, 856\n",
        "        ],\n",
        "        [\n",
        "            308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
        "            320, 1084, 514\n",
        "        ],\n",
        "        [\n",
        "            194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
        "            274, 810, 468\n",
        "        ],\n",
        "        [\n",
        "            536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
        "            730, 388, 1152, 354\n",
        "        ],\n",
        "        [\n",
        "            502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
        "            308, 650, 274, 844\n",
        "        ],\n",
        "        [\n",
        "            388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
        "            536, 388, 730\n",
        "        ],\n",
        "        [\n",
        "            354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
        "            342, 422, 536\n",
        "        ],\n",
        "        [\n",
        "            468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
        "            342, 0, 764, 194\n",
        "        ],\n",
        "        [\n",
        "            776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
        "            388, 422, 764, 0, 798\n",
        "        ],\n",
        "        [\n",
        "            662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
        "            536, 194, 798, 0\n",
        "        ],\n",
        "    ]\n",
        "    data['num_vehicles'] = 9\n",
        "    #data['depot'] = 0\n",
        "    data['demands'] = [0, 1, 1, 2, 4, 2, 5 , 8, 8, 1, 2, 1, 2, 4, 4, 8, 0]\n",
        "\n",
        "    data['vehicle_capacities'] = [14, 12, 16, 12, 15, 20,12,14,20]\n",
        "    data['min_capacity'] = [int(0.85*data['vehicle_capacities'][i]) for i in range(data['num_vehicles'])]\n",
        "    data['starts'] = [0]*data['num_vehicles']\n",
        "    data['ends'] = [16]*data['num_vehicles']\n",
        "    return data\n",
        "\n",
        "data = create_data_model()\n",
        "print(\"nodes\",len(data['distance_matrix']))\n",
        "print(\"demands: must be equal to nodes\",len(data['demands']))\n",
        "data['vehicle_capacities']\n",
        "\n",
        "print(data['min_capacity'])\n",
        "print(data['starts'])\n",
        "print(data['ends'])\n",
        "if len(data['vehicle_capacities']) == data['num_vehicles']:\n",
        "    print(\"bus np. == length of bus capacitites\", data['num_vehicles'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nodes 17\n",
            "demands: must be equal to nodes 17\n",
            "[11, 10, 13, 10, 12, 17, 10, 11, 17]\n",
            "[0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[16, 16, 16, 16, 16, 16, 16, 16, 16]\n",
            "bus np. == length of bus capacitites 9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NZX0Vna7H4Fs",
        "outputId": "351633f6-4e71-401b-b399-9be170b2d9ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "import numpy as np\n",
        "np.sum(data['demands'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rTurF1_5Yrcv",
        "colab": {}
      },
      "source": [
        "\n",
        "def print_solution(data, manager, routing, assignment):\n",
        "    \"\"\"Prints assignment on console.\"\"\"\n",
        "    print(\"objective cost: \", assignment.ObjectiveValue())\n",
        "    print()\n",
        "    plot_values = []\n",
        "    \n",
        "    total_distance = 0\n",
        "    total_load = 0\n",
        "    \n",
        "    for vehicle_id in range(data['num_vehicles']):\n",
        "        each_vehicle = []\n",
        "        index = routing.Start(vehicle_id)\n",
        "        plan_output = 'Route for vehicle {0} with minimum capacity {1}:\\n'.format(vehicle_id,data['min_capacity'][vehicle_id])\n",
        "        route_distance = 0\n",
        "        route_load = 0\n",
        "        \n",
        "        while not routing.IsEnd(index):\n",
        "            node_index = manager.IndexToNode(index)\n",
        "            each_vehicle.append(node_index)\n",
        "            route_load += data['demands'][node_index]\n",
        "            plan_output += ' {0} (Load({1})) -> '.format(node_index, route_load)\n",
        "            previous_index = index\n",
        "            index = assignment.Value(routing.NextVar(index))\n",
        "            \n",
        "            route_distance += routing.GetArcCostForVehicle(previous_index, index, vehicle_id)\n",
        "            \n",
        "        plan_output += ' {0} Load({1})\\n'.format(manager.IndexToNode(index),route_load)\n",
        "        \n",
        "        plan_output += 'Distance of the route: {}m\\n'.format(route_distance)\n",
        "        \n",
        "        plan_output += 'Load of the route: {}\\n'.format(route_load)\n",
        "    \n",
        "        if route_load != 0 and route_distance != 0:\n",
        "            each_vehicle.append(manager.IndexToNode(index))\n",
        "            print(plan_output)\n",
        "            print('\\n'+\"*\"*30)\n",
        "        \n",
        "        total_distance += route_distance\n",
        "        total_load += route_load\n",
        "        plot_values.append(each_vehicle)\n",
        "        \n",
        "        \n",
        "    print('Total distance of all routes: {}m'.format(total_distance))\n",
        "    print('Total load of all routes: {}'.format(total_load))\n",
        "    return plot_values\n",
        "    \n",
        "            \n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCnFuAeRMYt_",
        "colab_type": "text"
      },
      "source": [
        "## cost functions\n",
        "\n",
        " - SetAmortizedCostFactorsOfAllVehicles\n",
        "     - v: INDICATOR(v used in the model) * [linear_cost_factor_of_vehicle_[v] - quadratic_cost_factor_of_vehicle_[v]*(square of length of route v)]\n",
        "     \n",
        " - global_span_cost = coefficient * (Max(dimension_end_value) - Min(dimension start value))  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mehp79WGMYuA",
        "colab_type": "code",
        "outputId": "2d2078fb-2870-4a2b-e67f-21f7fae8f827",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "source": [
        "def main():\n",
        "    \n",
        "    data = create_data_model()\n",
        "\n",
        "    manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),data['num_vehicles'], data['starts'], data['ends'])\n",
        "\n",
        "    routing = pywrapcp.RoutingModel(manager)\n",
        "\n",
        "    routing.SetAmortizedCostFactorsOfAllVehicles(linear_cost_factor= 10*6, quadratic_cost_factor=1)\n",
        "    \n",
        "    \n",
        "    ###################### Dimension 1: Distance           #####################################\n",
        "    \n",
        "    def distance_callback(from_index, to_index):\n",
        "        \n",
        "        from_node = manager.IndexToNode(from_index)\n",
        "        to_node = manager.IndexToNode(to_index)\n",
        "        return data['distance_matrix'][from_node][to_node]\n",
        "\n",
        "    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
        "    \n",
        "    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
        "    \n",
        "    dimension_name = 'Distance'\n",
        "    routing.AddDimension(transit_callback_index,0, 3000, True, dimension_name)\n",
        "    distance_dimension = routing.GetDimensionOrDie(dimension_name)\n",
        "    distance_dimension.SetGlobalSpanCostCoefficient(7)\n",
        "    \n",
        "    \n",
        "    ####################          Dimension 2: Capacity          ####################################\n",
        "    capacity_dim = 'capacity'\n",
        "    def demand_callback(from_index):\n",
        "        from_node = manager.IndexToNode(from_index)\n",
        "        return data['demands'][from_node]\n",
        "\n",
        "    demand_callback_index = routing.RegisterUnaryTransitCallback(demand_callback)\n",
        "    \n",
        "    routing.AddDimensionWithVehicleCapacity(demand_callback_index,0, data['vehicle_capacities'], True,capacity_dim)\n",
        "    \n",
        "             #######     min capactity\n",
        "    capacity_dimension = routing.GetDimensionOrDie(capacity_dim)\n",
        "    minimum_allowed_capacity = data['min_capacity']\n",
        "    for vehicle in range(data['num_vehicles']):\n",
        "        capacity_dimension.CumulVar(routing.End(vehicle)).RemoveInterval(1, minimum_allowed_capacity[vehicle])\n",
        "    \n",
        "    #########################################################################################################\n",
        "    \n",
        "    #for vehicle in range(data['num_vehicles']):\n",
        "     #   routing.ConsiderEmptyRouteCostsForVehicle(True,vehicle)\n",
        "    # Setting first solution heuristic.\n",
        "    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
        "    search_parameters.first_solution_strategy = (routing_enums_pb2.FirstSolutionStrategy.LOCAL_CHEAPEST_ARC)\n",
        "\n",
        "    # Solve the problem.\n",
        "    solution = routing.SolveWithParameters(search_parameters)\n",
        "    #print(solution)\n",
        "    # Print solution on console.\n",
        "    if solution:\n",
        "        plot_values = print_solution(data, manager, routing, solution)\n",
        "    print(plot_values)\n",
        "    return plot_values, routing, search_parameters\n",
        "\n",
        "\n",
        "plot_values, routing, search_parameters = main()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "objective cost:  21697\n",
            "\n",
            "Route for vehicle 0 with minimum capacity 11:\n",
            " 0 (Load(0)) ->  5 (Load(2)) ->  9 (Load(3)) ->  12 (Load(5)) ->  11 (Load(6)) ->  15 (Load(14)) ->  16 Load(14)\n",
            "Distance of the route: 2122m\n",
            "Load of the route: 14\n",
            "\n",
            "\n",
            "******************************\n",
            "Route for vehicle 5 with minimum capacity 17:\n",
            " 0 (Load(0)) ->  8 (Load(8)) ->  6 (Load(13)) ->  2 (Load(14)) ->  10 (Load(16)) ->  14 (Load(20)) ->  16 Load(20)\n",
            "Distance of the route: 1758m\n",
            "Load of the route: 20\n",
            "\n",
            "\n",
            "******************************\n",
            "Route for vehicle 8 with minimum capacity 17:\n",
            " 0 (Load(0)) ->  1 (Load(1)) ->  3 (Load(3)) ->  4 (Load(7)) ->  7 (Load(15)) ->  13 (Load(19)) ->  16 Load(19)\n",
            "Distance of the route: 2214m\n",
            "Load of the route: 19\n",
            "\n",
            "\n",
            "******************************\n",
            "Total distance of all routes: 6094m\n",
            "Total load of all routes: 53\n",
            "[[0, 5, 9, 12, 11, 15, 16], [0], [0], [0], [0], [0, 8, 6, 2, 10, 14, 16], [0], [0], [0, 1, 3, 4, 7, 13, 16]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zP9Cj8qNJJ0M",
        "outputId": "b2586416-62db-4c5a-c467-fdac6a01f4b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        }
      },
      "source": [
        "import time\n",
        "def plot(plot_values):\n",
        "    plt.figure(figsize= [20,10])\n",
        "    plt.title(\" plot of different route take by buses\")\n",
        "    nodes = len(plot_values)\n",
        "    \n",
        "    \n",
        "    for i in range(nodes):\n",
        "        if len(plot_values[i]) != 1:\n",
        "            bus_i_route = plot_values[i].copy()\n",
        "            \n",
        "            start = bus_i_route[0]\n",
        "            end = bus_i_route[-1]\n",
        "             \n",
        "            bus_i_route.insert(0,start) #pad with first word before\n",
        "            bus_i_route.insert(-1,end)     #pad with end point after\n",
        "            print(bus_i_route)\n",
        "            plt.plot(bus_i_route[:-1],bus_i_route[1:])\n",
        "            for a,b in zip(bus_i_route[:-1], bus_i_route[1:]): \n",
        "                plt.text(a, b, str(b), fontsize=15)\n",
        "    plt.show()\n",
        "\n",
        "plot(plot_values)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 0, 5, 9, 12, 11, 15, 16, 16]\n",
            "[0, 0, 8, 6, 2, 10, 14, 16, 16]\n",
            "[0, 0, 1, 3, 4, 7, 13, 16, 16]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ERAah4EdEREREzkhzdzObazYHhzHvqN9Br+3FZVzMSJnBzdNvxuP2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4zTCC+ajcAouO5tmH81vD4JJi6ELsPB0xvCOlhXbdwuKMypfQD1gW2w/T9QWVL9Hu+gk8Ogql1D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\ngg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}